Overview

Dataset statistics

Number of variables12
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.5 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtde_invoices and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qtde_invoicesHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_item is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_products is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qtde_products and 1 other fieldsHigh correlation
qtde_item is highly skewed (γ1 = 28.46941323)Skewed
avg_ticket is highly skewed (γ1 = 53.44422362)Skewed
frequency is highly skewed (γ1 = 24.88049136)Skewed
qtde_returns is highly skewed (γ1 = 51.79774426)Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
qtde_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-05-25 02:40:45.609180
Analysis finished2023-05-25 02:41:47.339332
Duration1 minute and 1.73 second
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:47.627053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2023-05-24T23:41:48.004202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.3217
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:48.423351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.623
Coefficient of variation (CV)3.8484486
Kurtosis353.94472
Mean2749.3217
Median Absolute Deviation (MAD)672.16
Skewness16.777556
Sum8162736.2
Variance1.1194959 × 108
MonotonicityNot monotonic
2023-05-24T23:41:48.815288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.287639
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:49.227433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756779
Coefficient of variation (CV)1.2095137
Kurtosis2.7779627
Mean64.287639
Median Absolute Deviation (MAD)26
Skewness1.7983795
Sum190870
Variance6046.1167
MonotonicityNot monotonic
2023-05-24T23:41:49.624582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtde_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7231391
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:50.052566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8565313
Coefficient of variation (CV)1.5474954
Kurtosis190.83445
Mean5.7231391
Median Absolute Deviation (MAD)2
Skewness10.766805
Sum16992
Variance78.438147
MonotonicityNot monotonic
2023-05-24T23:41:50.433692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtde_item
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct747
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean384.94038
Minimum1
Maximum80996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:50.834839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q185
median154
Q3293
95-th percentile932.2
Maximum80996
Range80995
Interquartile range (IQR)208

Descriptive statistics

Standard deviation1941.885
Coefficient of variation (CV)5.0446383
Kurtosis1064.3611
Mean384.94038
Median Absolute Deviation (MAD)86
Skewness28.469413
Sum1142888
Variance3770917.4
MonotonicityNot monotonic
2023-05-24T23:41:51.265152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 28
 
0.9%
70 26
 
0.9%
67 24
 
0.8%
66 23
 
0.8%
90 22
 
0.7%
120 22
 
0.7%
52 21
 
0.7%
69 19
 
0.6%
75 19
 
0.6%
84 19
 
0.6%
Other values (737) 2746
92.5%
ValueCountFrequency (%)
1 3
0.1%
3 2
 
0.1%
6 2
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 4
0.1%
11 1
 
< 0.1%
12 6
0.2%
13 1
 
< 0.1%
15 2
 
0.1%
ValueCountFrequency (%)
80996 1
< 0.1%
38639 1
< 0.1%
21352 1
< 0.1%
17376 1
< 0.1%
17150 1
< 0.1%
16288 1
< 0.1%
15837 1
< 0.1%
13369 1
< 0.1%
12872 1
< 0.1%
10827 1
< 0.1%

qtde_products
Real number (ℝ)

Distinct341
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.323678
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:51.715508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q126
median52
Q3101
95-th percentile233.6
Maximum1786
Range1785
Interquartile range (IQR)75

Descriptive statistics

Standard deviation96.855131
Coefficient of variation (CV)1.2210116
Kurtosis82.402998
Mean79.323678
Median Absolute Deviation (MAD)33
Skewness6.3900489
Sum235512
Variance9380.9164
MonotonicityNot monotonic
2023-05-24T23:41:52.103660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 44
 
1.5%
37 39
 
1.3%
18 39
 
1.3%
28 39
 
1.3%
25 37
 
1.2%
26 37
 
1.2%
11 36
 
1.2%
15 36
 
1.2%
14 36
 
1.2%
30 36
 
1.2%
Other values (331) 2590
87.2%
ValueCountFrequency (%)
1 25
0.8%
2 16
0.5%
3 21
0.7%
4 20
0.7%
5 33
1.1%
6 23
0.8%
7 25
0.8%
8 30
1.0%
9 32
1.1%
10 27
0.9%
ValueCountFrequency (%)
1786 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
884 1
< 0.1%
817 1
< 0.1%
717 1
< 0.1%
714 1
< 0.1%
699 1
< 0.1%
636 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.897762
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:52.514066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.956587
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9344
Coefficient of variation (CV)19.98033
Kurtosis2890.7071
Mean51.897762
Median Absolute Deviation (MAD)5.984842
Skewness53.444224
Sum154084.45
Variance1075233
MonotonicityNot monotonic
2023-05-24T23:41:52.903686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.348511
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:53.327833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.923077
median48.285714
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.410256

Descriptive statistics

Standard deviation63.544929
Coefficient of variation (CV)0.94352388
Kurtosis4.8871091
Mean67.348511
Median Absolute Deviation (MAD)26.285714
Skewness2.0627709
Sum199957.73
Variance4037.958
MonotonicityNot monotonic
2023-05-24T23:41:53.728043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
11 17
 
0.6%
42 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137973
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:54.152464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049450549
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.03311068

Descriptive statistics

Standard deviation0.40815625
Coefficient of variation (CV)3.5866953
Kurtosis989.36508
Mean0.1137973
Median Absolute Deviation (MAD)0.012191338
Skewness24.880491
Sum337.8642
Variance0.16659153
MonotonicityNot monotonic
2023-05-24T23:41:54.545600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2636
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtde_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:54.997281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2023-05-24T23:41:55.403344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.81376
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:55.845488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.33333
Q3281.69231
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.44231

Descriptive statistics

Standard deviation791.55519
Coefficient of variation (CV)3.1685812
Kurtosis2255.5382
Mean249.81376
Median Absolute Deviation (MAD)83.083333
Skewness44.672717
Sum741697.07
Variance626559.62
MonotonicityNot monotonic
2023-05-24T23:41:56.263646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
136 8
 
0.3%
208 7
 
0.2%
Other values (1969) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct1005
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.154708
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-05-24T23:41:56.698741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.512322
Coefficient of variation (CV)0.88073027
Kurtosis27.703297
Mean22.154708
Median Absolute Deviation (MAD)8.2
Skewness3.4994559
Sum65777.329
Variance380.73071
MonotonicityNot monotonic
2023-05-24T23:41:57.093123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 39
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 32
 
1.1%
17 31
 
1.0%
18 30
 
1.0%
10 30
 
1.0%
5 29
 
1.0%
Other values (995) 2621
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

Interactions

2023-05-24T23:41:41.251724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:46.443225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:51.359193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:56.243561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:01.006851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:06.248233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:11.419427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:16.547869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:20.901729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:25.569626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:30.707152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:35.663768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:41.620352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:46.810749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:51.723777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:56.637400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:01.443522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:06.664598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:11.846336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:16.885474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:21.295398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:25.960773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:31.149183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:36.762758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:41.985450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:47.172374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:52.063237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:57.079067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:01.819672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:07.050747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:12.257644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:17.231317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:21.660832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:26.413918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:31.581330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:37.162152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:42.488689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:47.557519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:52.515879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:57.511131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:02.196808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:07.475047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:12.683109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:17.579638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:22.052982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:26.822018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:32.016479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:37.567688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:42.893846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:47.911618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:52.867116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:57.877269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:02.530974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:07.885978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:13.103506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:17.899243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:22.413419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:27.209163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:32.416304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:37.955047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:43.330187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:48.301524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:53.267920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:58.270300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:02.948122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:08.338665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:13.595410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:18.284389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:22.817010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:27.678622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:32.850912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:38.377194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:43.782373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:48.685064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:53.731085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:58.657448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:03.330268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:08.767810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:14.076151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:18.644184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:23.222167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:28.162904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:33.272061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:38.790234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:44.198773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:49.137308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:54.084710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:59.047596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:03.668663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:09.140738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:14.435702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:19.004793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:23.596300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:28.585315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:33.642726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:39.189587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:44.630695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:49.607154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:54.558405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:59.423288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:04.074810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:09.600888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:14.841105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:19.378930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:23.985904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:29.006329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:34.051400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:39.598742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:45.080022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:50.060880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:54.972553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:59.802151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:04.448954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:10.009050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:15.292253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:19.750083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:24.389268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:29.424016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:34.446549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:39.994886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:45.502172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:50.555281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:55.395513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:00.232822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:05.428274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:10.488336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:15.712395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:20.150759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:24.781414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:29.888759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:34.855461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:40.411597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:45.941063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:50.978812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:40:55.830412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:00.611449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:05.854946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:10.970064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:16.144490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:20.543113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:25.190566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:30.314006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:35.265622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-24T23:41:40.837363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-05-24T23:41:57.456741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0760.0010.026-0.0430.007-0.1310.019-0.002-0.063-0.123-0.007
gross_revenue-0.0761.000-0.4150.7700.7080.6640.246-0.2470.0900.3720.5740.291
recency_days0.001-0.4151.000-0.502-0.290-0.3800.0480.1080.018-0.120-0.098-0.106
qtde_invoices0.0260.770-0.5021.0000.5280.5830.059-0.2590.0790.2940.1000.025
qtde_item-0.0430.708-0.2900.5281.0000.3480.344-0.1670.0590.2570.6710.062
qtde_products0.0070.664-0.3800.5830.3481.000-0.456-0.113-0.0030.2070.3850.777
avg_ticket-0.1310.2460.0480.0590.344-0.4561.000-0.1220.0900.1900.188-0.611
avg_recency_days0.019-0.2470.108-0.259-0.167-0.113-0.1221.000-0.881-0.396-0.0770.048
frequency-0.0020.0900.0180.0790.059-0.0030.090-0.8811.0000.2340.027-0.072
qtde_returns-0.0630.372-0.1200.2940.2570.2070.190-0.3960.2341.0000.2100.019
avg_basket_size-0.1230.574-0.0980.1000.6710.3850.188-0.0770.0270.2101.0000.447
avg_unique_basket_size-0.0070.291-0.1060.0250.0620.777-0.6110.048-0.0720.0190.4471.000

Missing values

2023-05-24T23:41:46.468985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-24T23:41:47.052053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.035.021.018.15222235.50000017.00000040.050.9705888.735294
1130473232.5956.09.0131.0105.018.90403527.2500000.02830235.0154.44444419.000000
2125836705.382.015.01568.0114.028.90250023.1875000.04032350.0335.20000015.466667
313748948.2595.05.0169.024.033.86607192.6666670.0179210.087.8000005.600000
415100876.00333.03.048.01.0292.0000008.6000000.07317122.026.6666671.000000
5152914623.3025.014.0508.061.045.32647123.2000000.04011529.0150.1428577.285714
6146885630.877.021.0579.0148.017.21978618.3000000.057221399.0172.42857115.571429
7178095411.9116.012.0961.046.088.71983635.7000000.03352041.0171.4166675.083333
81531160767.900.091.02167.0567.025.5434644.1444440.243316474.0419.71428626.142857
9160982005.6387.07.0240.034.029.93477647.6666670.0243900.087.5714299.571429
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
5627177271060.2515.01.0111.066.016.0643946.01.0000006.0645.00000066.0
563717232421.522.02.066.030.011.70888912.00.1538460.0101.50000018.0
563817468137.0010.02.044.05.027.4000004.00.4000000.058.0000002.5
564913596697.045.02.081.0133.04.1990367.00.2500000.0203.00000083.0
5655148931237.859.02.0226.072.016.9568492.00.6666670.0399.50000036.5
565912479473.2011.01.087.030.015.7733334.01.00000034.0382.00000030.0
568014126706.137.03.0361.014.047.0753333.00.75000050.0169.3333335.0
5686135211092.391.03.046.0312.02.5112414.50.3000000.0244.333333145.0
569615060301.848.04.057.080.02.5153331.02.0000000.065.50000030.0
571512558269.967.01.0102.011.024.5418186.01.000000196.0196.00000011.0